Triple

T11299737
Position Surface form Disambiguated ID Type / Status
Subject Hair (1979 film) E267551 entity
Predicate starredActor P5563 FINISHED
Object Treat Williams E322049 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Treat Williams | Statement: [Hair (1979 film), starredActor, Treat Williams]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Treat Williams
Context triple: [Hair (1979 film), starredActor, Treat Williams]
  • A. Treat Williams chosen
    Treat Williams was an American actor known for his versatile performances in film, television, and theater, including notable roles in works like "Hair," "Prince of the City," and the TV series "Everwood."
  • B. Danny Williams
    Danny Williams was a British pop singer best known for his 1961 hit rendition of "Moon River."
  • C. Danny Williams
    Danny Williams is the fictional nightclub entertainer and family man portrayed by Danny Thomas on the classic American television sitcom "The Danny Thomas Show."
  • D. Jerry Williams Jr.
    Jerry Williams Jr. was an American soul and R&B singer, songwriter, and producer, better known by his stage name Swamp Dogg.
  • E. Aon Hewitt
    Aon Hewitt is a global human resources consulting and outsourcing firm known for its work in benefits, talent, and retirement solutions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9a3616c8190a8fd23ca67463806 completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a4af56881908cc395b6687d40a9 completed April 19, 2026, 5 p.m.
Created at: April 8, 2026, 9:32 p.m.